This work focuses on relative comparisons of individual methods performance. It is based on Stochastic Performance Logic, which allows to express, for example, that one method runs at most two times longer than another method. This results are more portable than absolute values. It extends standard unit tests with performance assumptions, which are evaluated during actual run-time of a released application. Dynamically added and removed instrumentation is used for automatic modification of the production code. Instrumentation part uses DiSL framework to be able to seamlessly measure even Java system classes. Methods are measured sequentially, number of concurrently measured method is dynamically changed and measurement code is removed as soon as required data are obtained to avoid high overhead. The results show that for processor demanding application this approach may bring up to 3-times lower overhead peaks than measuring all methods at once. Powered by TCPDF (www.tcpdf.org)

This work focuses on relative comparisons of individual method's performance. It is based on Stochastic Performance Logic, which allows to express, for example, that one method runs at most two times longer than another method. These results are more portable than absolute values. It extends standard unit tests with performance assumptions, which are evaluated during actual run-time of a released application. Dynamically added and removed instrumentation is used for automatic modification of the production code. Instrumentation part uses DiSL framework to be able to seamlessly measure even Java system classes. Methods are measured sequentially, the number of concurrently measured methods is dynamically changed and the measurement code is removed as soon as required data are obtained. The results show that this approach may bring appreciably lower overhead peaks than measuring all methods at once. The prototype was compared with JMH tool and the results show that it is able to accurately measure methods running longer than 1 ms. Powered by TCPDF (www.tcpdf.org)

The use of distributed systems, involving multiple components, has become a common industry practice. However, modeling the behaviour of such systems is a challenge, especially when the behavior consists of several collaborations of different parties, each involving possibly several starting (input) and ending (output) events of the involved components. Furthermore, the global behavior should be described as a composition of several sub-behaviours, in the following called collaborations, and each collaboration may be further decomposed into several sub-collaborations. We assume that the performance of the elementary sub-collaborations is known, and that the performance of the global behavior should be determined from the performance of the contained elementary collaborations and the form of the composition. A collaboration, in this thesis, is characterized by a partial order of input and output events, and the performance of the collaboration is defined by the minimum delays required for a given output event with respect to an input event. This is a generalization of the semantics of UML Activities, where all input events are assumed to occur at the same time, and all output events occur at the same time. We give a semantic definition of the dynamic behavior of composed collaborations using the composition operators for control flow from UML Activity diagrams, in terms of partial order relationships among the involved input and output events. Based on these semantics, we provide formulas for calculating the performance of composed collaborations in terms of the performance of the sub-collaborations, where each delay is characterized by (a) a fixed value, (b) a range of values, and (c) a distribution (in the case of stochastic behaviours). We also propose approximations for the case of stochastic behavior with Normal distributions, and discuss the expected errors that may be introduced due to ignoring of shared resources or possible dependencies in the case of stochastic behaviours. A tool has been developed for evaluating the performance of complex collaborations, and examples and case studies are discussed to illustrate the applicability of the performance analysis and the visual notation which we introduced for representing the partial-order relationships of the input and output events.

The use of distributed systems, involving multiple components, has become a common industry practice. However, modeling the behaviour of such systems is a challenge, especially when the behavior consists of several collaborations of different parties, each involving possibly several starting (input) and ending (output) events of the involved components. Furthermore, the global behavior should be described as a composition of several sub-behaviours, in the following called collaborations, and each collaboration may be further decomposed into several sub-collaborations. We assume that the performance of the elementary sub-collaborations is known, and that the performance of the global behavior should be determined from the performance of the contained elementary collaborations and the form of the composition. A collaboration, in this thesis, is characterized by a partial order of input and output events, and the performance of the collaboration is defined by the minimum delays required for a given output event with respect to an input event. This is a generalization of the semantics of UML Activities, where all input events are assumed to occur at the same time, and all output events occur at the same time. We give a semantic definition of the dynamic behavior of composed collaborations using the composition operators for control flow from UML Activity diagrams, in terms of partial order relationships among the involved input and output events. Based on these semantics, we provide formulas for calculating the performance of composed collaborations in terms of the performance of the sub-collaborations, where each delay is characterized by (a) a fixed value, (b) a range of values, and (c) a distribution (in the case of stochastic behaviours). We also propose approximations for the case of stochastic behavior with Normal distributions, and discuss the expected errors that may be introduced due to ignoring of shared resources or possible dependencies in the case of stochastic behaviours. A tool has been developed for evaluating the performance of complex collaborations, and examples and case studies are discussed to illustrate the applicability of the performance analysis and the visual notation which we introduced for representing the partial-order relationships of the input and output events.